Overview

Dataset statistics

Number of variables19
Number of observations7766
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 MiB
Average record size in memory152.0 B

Variable types

Categorical6
Numeric13

Alerts

track has a high cardinality: 7396 distinct valuesHigh cardinality
artist has a high cardinality: 1837 distinct valuesHigh cardinality
uri has a high cardinality: 7749 distinct valuesHigh cardinality
danceability is highly overall correlated with valenceHigh correlation
energy is highly overall correlated with loudness and 2 other fieldsHigh correlation
loudness is highly overall correlated with energy and 1 other fieldsHigh correlation
acousticness is highly overall correlated with energy and 1 other fieldsHigh correlation
valence is highly overall correlated with danceability and 1 other fieldsHigh correlation
duration_ms is highly overall correlated with sectionsHigh correlation
sections is highly overall correlated with duration_msHigh correlation
track is uniformly distributedUniform
uri is uniformly distributedUniform
target is uniformly distributedUniform
key has 1016 (13.1%) zerosZeros
instrumentalness has 1441 (18.6%) zerosZeros

Reproduction

Analysis started2022-11-29 22:50:35.103801
Analysis finished2022-11-29 22:50:52.260452
Duration17.16 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

track
Categorical

HIGH CARDINALITY
UNIFORM

Distinct7396
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Memory size60.8 KiB
Sunshine
 
5
Love Song
 
4
Hold On
 
4
Superman
 
4
Vai Trabalhar Vagabundo - Ao Vivo
 
4
Other values (7391)
7745 

Length

Max length254
Median length95.5
Mean length19.737317
Min length1

Characters and Unicode

Total characters153280
Distinct characters124
Distinct categories13 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7058 ?
Unique (%)90.9%

Sample

1st rowPorque?
2nd rowFree Jazz
3rd rowReject of Society
4th rowYeah !
5th rowGotta Find A Way

Common Values

ValueCountFrequency (%)
Sunshine 5
 
0.1%
Love Song 4
 
0.1%
Hold On 4
 
0.1%
Superman 4
 
0.1%
Vai Trabalhar Vagabundo - Ao Vivo 4
 
0.1%
Happy 4
 
0.1%
Angel 3
 
< 0.1%
Until It's Time For You To Go 3
 
< 0.1%
You 3
 
< 0.1%
Woodstock 3
 
< 0.1%
Other values (7386) 7729
99.5%

Length

2022-11-29T17:50:52.344144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 985
 
3.3%
887
 
3.0%
you 554
 
1.9%
love 456
 
1.5%
i 452
 
1.5%
a 449
 
1.5%
to 373
 
1.3%
me 365
 
1.2%
in 329
 
1.1%
of 327
 
1.1%
Other values (6836) 24327
82.5%

Most occurring characters

ValueCountFrequency (%)
21738
 
14.2%
e 14383
 
9.4%
o 9882
 
6.4%
a 9201
 
6.0%
n 8083
 
5.3%
i 7128
 
4.7%
r 6540
 
4.3%
t 6256
 
4.1%
l 4803
 
3.1%
s 4801
 
3.1%
Other values (114) 60465
39.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 98561
64.3%
Uppercase Letter 26913
 
17.6%
Space Separator 21738
 
14.2%
Other Punctuation 2550
 
1.7%
Decimal Number 1464
 
1.0%
Dash Punctuation 842
 
0.5%
Open Punctuation 594
 
0.4%
Close Punctuation 594
 
0.4%
Math Symbol 8
 
< 0.1%
Final Punctuation 6
 
< 0.1%
Other values (3) 10
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 14383
14.6%
o 9882
10.0%
a 9201
 
9.3%
n 8083
 
8.2%
i 7128
 
7.2%
r 6540
 
6.6%
t 6256
 
6.3%
l 4803
 
4.9%
s 4801
 
4.9%
u 3828
 
3.9%
Other values (42) 23656
24.0%
Uppercase Letter
ValueCountFrequency (%)
T 2625
 
9.8%
S 2024
 
7.5%
M 1961
 
7.3%
L 1829
 
6.8%
A 1723
 
6.4%
I 1700
 
6.3%
B 1616
 
6.0%
D 1378
 
5.1%
C 1298
 
4.8%
W 1211
 
4.5%
Other values (23) 9548
35.5%
Other Punctuation
ValueCountFrequency (%)
' 1293
50.7%
, 394
 
15.5%
. 269
 
10.5%
/ 196
 
7.7%
: 133
 
5.2%
" 99
 
3.9%
& 62
 
2.4%
? 50
 
2.0%
! 28
 
1.1%
; 10
 
0.4%
Other values (5) 16
 
0.6%
Decimal Number
ValueCountFrequency (%)
0 300
20.5%
1 299
20.4%
2 273
18.6%
9 186
12.7%
7 108
 
7.4%
6 74
 
5.1%
5 73
 
5.0%
4 58
 
4.0%
3 55
 
3.8%
8 38
 
2.6%
Open Punctuation
ValueCountFrequency (%)
( 586
98.7%
[ 8
 
1.3%
Close Punctuation
ValueCountFrequency (%)
) 586
98.7%
] 8
 
1.3%
Math Symbol
ValueCountFrequency (%)
+ 5
62.5%
= 3
37.5%
Other Letter
ValueCountFrequency (%)
º 3
60.0%
ª 2
40.0%
Other Symbol
ValueCountFrequency (%)
° 2
66.7%
¦ 1
33.3%
Space Separator
ValueCountFrequency (%)
21738
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 842
100.0%
Final Punctuation
ValueCountFrequency (%)
6
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 125479
81.9%
Common 27801
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 14383
 
11.5%
o 9882
 
7.9%
a 9201
 
7.3%
n 8083
 
6.4%
i 7128
 
5.7%
r 6540
 
5.2%
t 6256
 
5.0%
l 4803
 
3.8%
s 4801
 
3.8%
u 3828
 
3.1%
Other values (77) 50574
40.3%
Common
ValueCountFrequency (%)
21738
78.2%
' 1293
 
4.7%
- 842
 
3.0%
( 586
 
2.1%
) 586
 
2.1%
, 394
 
1.4%
0 300
 
1.1%
1 299
 
1.1%
2 273
 
1.0%
. 269
 
1.0%
Other values (27) 1221
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 152647
99.6%
None 625
 
0.4%
Punctuation 8
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
21738
 
14.2%
e 14383
 
9.4%
o 9882
 
6.5%
a 9201
 
6.0%
n 8083
 
5.3%
i 7128
 
4.7%
r 6540
 
4.3%
t 6256
 
4.1%
l 4803
 
3.1%
s 4801
 
3.1%
Other values (73) 59832
39.2%
None
ValueCountFrequency (%)
ó 100
16.0%
é 94
15.0%
ç 90
14.4%
ã 57
9.1%
í 50
8.0%
á 48
7.7%
ê 27
 
4.3%
ñ 26
 
4.2%
è 19
 
3.0%
É 15
 
2.4%
Other values (29) 99
15.8%
Punctuation
ValueCountFrequency (%)
6
75.0%
2
 
25.0%

artist
Categorical

Distinct1837
Distinct (%)23.7%
Missing0
Missing (%)0.0%
Memory size60.8 KiB
MPB4
 
73
kalapana
 
54
Reginaldo Rossi
 
53
Buzzcocks
 
50
Vicente Fernández
 
44
Other values (1832)
7492 

Length

Max length67
Median length45
Mean length12.960211
Min length1

Characters and Unicode

Total characters100649
Distinct characters84
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique766 ?
Unique (%)9.9%

Sample

1st rowReginaldo Rossi
2nd rowMaria Teresa Luciani
3rd rowCrass
4th rowThe Jets
5th rowThe Moments

Common Values

ValueCountFrequency (%)
MPB4 73
 
0.9%
kalapana 54
 
0.7%
Reginaldo Rossi 53
 
0.7%
Buzzcocks 50
 
0.6%
Vicente Fernández 44
 
0.6%
John Coltrane 44
 
0.6%
Philip Glass 43
 
0.6%
Stephen Sondheim 41
 
0.5%
Traditional 40
 
0.5%
De Wolfe Music 40
 
0.5%
Other values (1827) 7284
93.8%

Length

2022-11-29T17:50:52.466991image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
the 882
 
5.3%
330
 
2.0%
john 214
 
1.3%
band 145
 
0.9%
de 106
 
0.6%
mpb4 73
 
0.4%
davis 66
 
0.4%
brothers 66
 
0.4%
orchestra 64
 
0.4%
joe 64
 
0.4%
Other values (2464) 14725
88.0%

Most occurring characters

ValueCountFrequency (%)
e 9407
 
9.3%
8969
 
8.9%
a 7611
 
7.6%
n 6462
 
6.4%
o 6381
 
6.3%
i 6065
 
6.0%
r 5919
 
5.9%
l 4412
 
4.4%
s 4322
 
4.3%
t 3740
 
3.7%
Other values (74) 37361
37.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 73175
72.7%
Uppercase Letter 17349
 
17.2%
Space Separator 8969
 
8.9%
Other Punctuation 845
 
0.8%
Decimal Number 191
 
0.2%
Dash Punctuation 108
 
0.1%
Open Punctuation 6
 
< 0.1%
Close Punctuation 6
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9407
12.9%
a 7611
10.4%
n 6462
8.8%
o 6381
8.7%
i 6065
 
8.3%
r 5919
 
8.1%
l 4412
 
6.0%
s 4322
 
5.9%
t 3740
 
5.1%
h 2970
 
4.1%
Other values (29) 15886
21.7%
Uppercase Letter
ValueCountFrequency (%)
B 1606
 
9.3%
T 1599
 
9.2%
S 1510
 
8.7%
M 1224
 
7.1%
C 1186
 
6.8%
J 1054
 
6.1%
R 993
 
5.7%
D 992
 
5.7%
G 791
 
4.6%
L 782
 
4.5%
Other values (16) 5612
32.3%
Decimal Number
ValueCountFrequency (%)
4 77
40.3%
9 55
28.8%
5 19
 
9.9%
6 16
 
8.4%
0 12
 
6.3%
1 9
 
4.7%
3 2
 
1.0%
8 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
& 331
39.2%
. 305
36.1%
' 105
 
12.4%
, 54
 
6.4%
" 32
 
3.8%
/ 9
 
1.1%
! 9
 
1.1%
Space Separator
ValueCountFrequency (%)
8969
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 108
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 90524
89.9%
Common 10125
 
10.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9407
 
10.4%
a 7611
 
8.4%
n 6462
 
7.1%
o 6381
 
7.0%
i 6065
 
6.7%
r 5919
 
6.5%
l 4412
 
4.9%
s 4322
 
4.8%
t 3740
 
4.1%
h 2970
 
3.3%
Other values (55) 33235
36.7%
Common
ValueCountFrequency (%)
8969
88.6%
& 331
 
3.3%
. 305
 
3.0%
- 108
 
1.1%
' 105
 
1.0%
4 77
 
0.8%
9 55
 
0.5%
, 54
 
0.5%
" 32
 
0.3%
5 19
 
0.2%
Other values (9) 70
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100330
99.7%
None 319
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
e 9407
 
9.4%
8969
 
8.9%
a 7611
 
7.6%
n 6462
 
6.4%
o 6381
 
6.4%
i 6065
 
6.0%
r 5919
 
5.9%
l 4412
 
4.4%
s 4322
 
4.3%
t 3740
 
3.7%
Other values (61) 37042
36.9%
None
ValueCountFrequency (%)
é 78
24.5%
á 56
17.6%
í 53
16.6%
ó 33
10.3%
ç 26
 
8.2%
à 23
 
7.2%
ô 23
 
7.2%
ã 9
 
2.8%
ł 7
 
2.2%
ñ 5
 
1.6%
Other values (3) 6
 
1.9%

uri
Categorical

HIGH CARDINALITY
UNIFORM

Distinct7749
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size60.8 KiB
spotify:track:3mRM4NM8iO7UBqrSigCQFH
 
3
spotify:track:4M1zcX803dgkDkISHZDPYi
 
2
spotify:track:1QR3Wcba5NBidmxEE8DW3w
 
2
spotify:track:7cRioMuuOoe8rwcqvsM4pD
 
2
spotify:track:1K1nzhbKCNmrNXi9B07mPF
 
2
Other values (7744)
7755 

Length

Max length36
Median length36
Mean length36
Min length36

Characters and Unicode

Total characters279576
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7733 ?
Unique (%)99.6%

Sample

1st rowspotify:track:28KC2wl7jDv6Ms1Uc0OMpm
2nd rowspotify:track:0zagn8PNnM7pM8pz1gXOAD
3rd rowspotify:track:18eNASEiuAhO2ML5LBIoG4
4th rowspotify:track:1UlJzHc8E5EprQFJxjSyPL
5th rowspotify:track:1Jtlirju8tUXTtvyZJAN5v

Common Values

ValueCountFrequency (%)
spotify:track:3mRM4NM8iO7UBqrSigCQFH 3
 
< 0.1%
spotify:track:4M1zcX803dgkDkISHZDPYi 2
 
< 0.1%
spotify:track:1QR3Wcba5NBidmxEE8DW3w 2
 
< 0.1%
spotify:track:7cRioMuuOoe8rwcqvsM4pD 2
 
< 0.1%
spotify:track:1K1nzhbKCNmrNXi9B07mPF 2
 
< 0.1%
spotify:track:3sWHFwTUKWTwETu4QmD8bb 2
 
< 0.1%
spotify:track:7fu3Tv5rcoGD1PZV7s57WW 2
 
< 0.1%
spotify:track:3iJGrfXRepS04IiZP3VBUp 2
 
< 0.1%
spotify:track:5tmrnhMxx39jnu3kjopMdP 2
 
< 0.1%
spotify:track:2ljjoZun9Xid1AAFKEEXam 2
 
< 0.1%
Other values (7739) 7745
99.7%

Length

2022-11-29T17:50:52.763190image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
spotify:track:3mrm4nm8io7ubqrsigcqfh 3
 
< 0.1%
spotify:track:2ljjozun9xid1aafkeexam 2
 
< 0.1%
spotify:track:1navd19eofgpff6wosmhie 2
 
< 0.1%
spotify:track:1hjnicn5cvl9x8u44xumqe 2
 
< 0.1%
spotify:track:2iop69pwuhyotcachebhko 2
 
< 0.1%
spotify:track:4xxn8gdqs7ruwgztnznxnp 2
 
< 0.1%
spotify:track:0rgcouqg4qyaet9ridf3ob 2
 
< 0.1%
spotify:track:2t3r19v9qmiiurwpor8axf 2
 
< 0.1%
spotify:track:4m1zcx803dgkdkishzdpyi 2
 
< 0.1%
spotify:track:5tmrnhmxx39jnu3kjopmdp 2
 
< 0.1%
Other values (7739) 7745
99.7%

Most occurring characters

ValueCountFrequency (%)
t 18236
 
6.5%
: 15532
 
5.6%
p 10499
 
3.8%
c 10487
 
3.8%
s 10474
 
3.7%
f 10465
 
3.7%
i 10465
 
3.7%
a 10452
 
3.7%
o 10440
 
3.7%
y 10362
 
3.7%
Other values (53) 162164
58.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 162142
58.0%
Uppercase Letter 67647
24.2%
Decimal Number 34255
 
12.3%
Other Punctuation 15532
 
5.6%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
t 18236
 
11.2%
p 10499
 
6.5%
c 10487
 
6.5%
s 10474
 
6.5%
f 10465
 
6.5%
i 10465
 
6.5%
a 10452
 
6.4%
o 10440
 
6.4%
y 10362
 
6.4%
k 10349
 
6.4%
Other values (16) 49913
30.8%
Uppercase Letter
ValueCountFrequency (%)
V 2716
 
4.0%
C 2671
 
3.9%
Q 2653
 
3.9%
E 2643
 
3.9%
M 2641
 
3.9%
N 2637
 
3.9%
H 2633
 
3.9%
Y 2633
 
3.9%
F 2630
 
3.9%
J 2624
 
3.9%
Other values (16) 41166
60.9%
Decimal Number
ValueCountFrequency (%)
6 3684
10.8%
4 3675
10.7%
1 3668
10.7%
5 3654
10.7%
2 3638
10.6%
0 3627
10.6%
3 3583
10.5%
7 3414
10.0%
9 2676
7.8%
8 2636
7.7%
Other Punctuation
ValueCountFrequency (%)
: 15532
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 229789
82.2%
Common 49787
 
17.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
t 18236
 
7.9%
p 10499
 
4.6%
c 10487
 
4.6%
s 10474
 
4.6%
f 10465
 
4.6%
i 10465
 
4.6%
a 10452
 
4.5%
o 10440
 
4.5%
y 10362
 
4.5%
k 10349
 
4.5%
Other values (42) 117560
51.2%
Common
ValueCountFrequency (%)
: 15532
31.2%
6 3684
 
7.4%
4 3675
 
7.4%
1 3668
 
7.4%
5 3654
 
7.3%
2 3638
 
7.3%
0 3627
 
7.3%
3 3583
 
7.2%
7 3414
 
6.9%
9 2676
 
5.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 279576
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 18236
 
6.5%
: 15532
 
5.6%
p 10499
 
3.8%
c 10487
 
3.8%
s 10474
 
3.7%
f 10465
 
3.7%
i 10465
 
3.7%
a 10452
 
3.7%
o 10440
 
3.7%
y 10362
 
3.7%
Other values (53) 162164
58.0%

danceability
Real number (ℝ)

Distinct803
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52550045
Minimum0.063
Maximum0.961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:52.854246image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.063
5-th percentile0.243
Q10.415
median0.5345
Q30.646
95-th percentile0.779
Maximum0.961
Range0.898
Interquartile range (IQR)0.231

Descriptive statistics

Standard deviation0.16281663
Coefficient of variation (CV)0.30983157
Kurtosis-0.39999904
Mean0.52550045
Median Absolute Deviation (MAD)0.1155
Skewness-0.2205781
Sum4081.0365
Variance0.026509255
MonotonicityNot monotonic
2022-11-29T17:50:52.954917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.567 30
 
0.4%
0.62 28
 
0.4%
0.55 27
 
0.3%
0.671 26
 
0.3%
0.592 26
 
0.3%
0.624 25
 
0.3%
0.485 25
 
0.3%
0.417 25
 
0.3%
0.628 25
 
0.3%
0.465 25
 
0.3%
Other values (793) 7504
96.6%
ValueCountFrequency (%)
0.063 1
< 0.1%
0.0633 1
< 0.1%
0.0649 1
< 0.1%
0.0651 1
< 0.1%
0.0667 1
< 0.1%
0.0675 1
< 0.1%
0.0691 1
< 0.1%
0.0697 1
< 0.1%
0.0707 1
< 0.1%
0.0739 1
< 0.1%
ValueCountFrequency (%)
0.961 1
< 0.1%
0.951 1
< 0.1%
0.948 1
< 0.1%
0.942 1
< 0.1%
0.94 1
< 0.1%
0.93 1
< 0.1%
0.922 2
< 0.1%
0.92 1
< 0.1%
0.917 1
< 0.1%
0.913 1
< 0.1%

energy
Real number (ℝ)

Distinct1165
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.52795226
Minimum0.0011
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:53.048779image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0011
5-th percentile0.12025
Q10.354
median0.533
Q30.712
95-th percentile0.902
Maximum0.999
Range0.9979
Interquartile range (IQR)0.358

Descriptive statistics

Standard deviation0.2354931
Coefficient of variation (CV)0.44604999
Kurtosis-0.77428655
Mean0.52795226
Median Absolute Deviation (MAD)0.179
Skewness-0.14305289
Sum4100.0773
Variance0.055457
MonotonicityNot monotonic
2022-11-29T17:50:53.150662image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.623 23
 
0.3%
0.564 22
 
0.3%
0.444 22
 
0.3%
0.641 22
 
0.3%
0.396 21
 
0.3%
0.526 21
 
0.3%
0.443 20
 
0.3%
0.52 20
 
0.3%
0.62 19
 
0.2%
0.72 19
 
0.2%
Other values (1155) 7557
97.3%
ValueCountFrequency (%)
0.0011 1
< 0.1%
0.00131 1
< 0.1%
0.00193 1
< 0.1%
0.00209 1
< 0.1%
0.00325 1
< 0.1%
0.00334 1
< 0.1%
0.00358 1
< 0.1%
0.00383 1
< 0.1%
0.00393 2
< 0.1%
0.00396 1
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.998 3
< 0.1%
0.997 1
 
< 0.1%
0.995 5
0.1%
0.993 1
 
< 0.1%
0.992 2
 
< 0.1%
0.991 3
< 0.1%
0.99 1
 
< 0.1%
0.989 3
< 0.1%
0.988 4
0.1%

key
Real number (ℝ)

Distinct12
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.2083441
Minimum0
Maximum11
Zeros1016
Zeros (%)13.1%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:53.232402image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q39
95-th percentile11
Maximum11
Range11
Interquartile range (IQR)7

Descriptive statistics

Standard deviation3.5044797
Coefficient of variation (CV)0.67285871
Kurtosis-1.279148
Mean5.2083441
Median Absolute Deviation (MAD)3
Skewness-0.0022877076
Sum40448
Variance12.281378
MonotonicityNot monotonic
2022-11-29T17:50:53.301705image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
2 1045
13.5%
9 1023
13.2%
0 1016
13.1%
7 991
12.8%
5 759
9.8%
4 687
8.8%
10 468
6.0%
11 468
6.0%
1 421
5.4%
8 380
 
4.9%
Other values (2) 508
6.5%
ValueCountFrequency (%)
0 1016
13.1%
1 421
5.4%
2 1045
13.5%
3 222
 
2.9%
4 687
8.8%
5 759
9.8%
6 286
 
3.7%
7 991
12.8%
8 380
 
4.9%
9 1023
13.2%
ValueCountFrequency (%)
11 468
6.0%
10 468
6.0%
9 1023
13.2%
8 380
 
4.9%
7 991
12.8%
6 286
 
3.7%
5 759
9.8%
4 687
8.8%
3 222
 
2.9%
2 1045
13.5%

loudness
Real number (ℝ)

Distinct6021
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-11.73427
Minimum-41.44
Maximum3.744
Zeros0
Zeros (%)0.0%
Negative7761
Negative (%)99.9%
Memory size60.8 KiB
2022-11-29T17:50:53.385366image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum-41.44
5-th percentile-20.73575
Q1-14.07
median-11.046
Q3-8.4
95-th percentile-5.4515
Maximum3.744
Range45.184
Interquartile range (IQR)5.67

Descriptive statistics

Standard deviation4.8159992
Coefficient of variation (CV)-0.41042171
Kurtosis2.5899224
Mean-11.73427
Median Absolute Deviation (MAD)2.812
Skewness-1.1886353
Sum-91128.341
Variance23.193848
MonotonicityNot monotonic
2022-11-29T17:50:53.477066image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9.04 6
 
0.1%
-9.037 6
 
0.1%
-13.507 5
 
0.1%
-12.621 5
 
0.1%
-11.589 5
 
0.1%
-11.675 5
 
0.1%
-9.744 4
 
0.1%
-9.997 4
 
0.1%
-12.71 4
 
0.1%
-6.339 4
 
0.1%
Other values (6011) 7718
99.4%
ValueCountFrequency (%)
-41.44 1
< 0.1%
-38.201 1
< 0.1%
-37.012 1
< 0.1%
-36.35 1
< 0.1%
-36.179 1
< 0.1%
-35.983 1
< 0.1%
-35.641 1
< 0.1%
-35.636 1
< 0.1%
-35.347 1
< 0.1%
-35.298 1
< 0.1%
ValueCountFrequency (%)
3.744 1
< 0.1%
2.291 1
< 0.1%
1.963 1
< 0.1%
0.878 1
< 0.1%
0.45 1
< 0.1%
-0.93 1
< 0.1%
-0.983 1
< 0.1%
-1.044 1
< 0.1%
-1.608 1
< 0.1%
-1.734 1
< 0.1%

mode
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.8 KiB
1
5607 
0
2159 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5607
72.2%
0 2159
 
27.8%

Length

2022-11-29T17:50:53.568917image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:50:53.640130image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
1 5607
72.2%
0 2159
 
27.8%

Most occurring characters

ValueCountFrequency (%)
1 5607
72.2%
0 2159
 
27.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5607
72.2%
0 2159
 
27.8%

Most occurring scripts

ValueCountFrequency (%)
Common 7766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5607
72.2%
0 2159
 
27.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 5607
72.2%
0 2159
 
27.8%

speechiness
Real number (ℝ)

Distinct1004
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.062052975
Minimum0.0225
Maximum0.952
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:53.721475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0225
5-th percentile0.0278
Q10.0335
median0.0416
Q30.060475
95-th percentile0.16
Maximum0.952
Range0.9295
Interquartile range (IQR)0.026975

Descriptive statistics

Standard deviation0.069836519
Coefficient of variation (CV)1.1254339
Kurtosis49.308551
Mean0.062052975
Median Absolute Deviation (MAD)0.0103
Skewness5.9075716
Sum481.9034
Variance0.0048771394
MonotonicityNot monotonic
2022-11-29T17:50:53.821356image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0312 41
 
0.5%
0.0346 40
 
0.5%
0.029 39
 
0.5%
0.0354 37
 
0.5%
0.0322 37
 
0.5%
0.0294 37
 
0.5%
0.033 36
 
0.5%
0.0301 36
 
0.5%
0.0287 36
 
0.5%
0.0335 35
 
0.5%
Other values (994) 7392
95.2%
ValueCountFrequency (%)
0.0225 1
 
< 0.1%
0.0227 1
 
< 0.1%
0.0232 3
< 0.1%
0.0233 1
 
< 0.1%
0.0235 1
 
< 0.1%
0.0236 1
 
< 0.1%
0.0237 1
 
< 0.1%
0.0238 2
< 0.1%
0.0239 3
< 0.1%
0.024 2
< 0.1%
ValueCountFrequency (%)
0.952 1
< 0.1%
0.947 1
< 0.1%
0.933 1
< 0.1%
0.928 1
< 0.1%
0.921 2
< 0.1%
0.908 1
< 0.1%
0.897 1
< 0.1%
0.891 1
< 0.1%
0.889 1
< 0.1%
0.868 1
< 0.1%

acousticness
Real number (ℝ)

Distinct2004
Distinct (%)25.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.43424032
Minimum1.35 × 10-6
Maximum0.996
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:53.923321image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum1.35 × 10-6
5-th percentile0.0055225
Q10.124
median0.409
Q30.726
95-th percentile0.957
Maximum0.996
Range0.99599865
Interquartile range (IQR)0.602

Descriptive statistics

Standard deviation0.32150036
Coefficient of variation (CV)0.74037428
Kurtosis-1.3300312
Mean0.43424032
Median Absolute Deviation (MAD)0.298
Skewness0.19623582
Sum3372.3103
Variance0.10336248
MonotonicityNot monotonic
2022-11-29T17:50:54.017133image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.994 23
 
0.3%
0.995 21
 
0.3%
0.99 17
 
0.2%
0.992 16
 
0.2%
0.107 16
 
0.2%
0.208 16
 
0.2%
0.678 16
 
0.2%
0.116 15
 
0.2%
0.891 14
 
0.2%
0.184 14
 
0.2%
Other values (1994) 7598
97.8%
ValueCountFrequency (%)
1.35 × 10-61
< 0.1%
1.67 × 10-61
< 0.1%
1.78 × 10-61
< 0.1%
1.83 × 10-61
< 0.1%
1.86 × 10-61
< 0.1%
2.2 × 10-61
< 0.1%
2.41 × 10-61
< 0.1%
3 × 10-61
< 0.1%
3.54 × 10-61
< 0.1%
3.83 × 10-61
< 0.1%
ValueCountFrequency (%)
0.996 5
 
0.1%
0.995 21
0.3%
0.994 23
0.3%
0.993 11
0.1%
0.992 16
0.2%
0.991 10
0.1%
0.99 17
0.2%
0.989 10
0.1%
0.988 9
 
0.1%
0.987 10
0.1%

instrumentalness
Real number (ℝ)

Distinct3221
Distinct (%)41.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16216257
Minimum0
Maximum0.998
Zeros1441
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:54.119135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.445 × 10-6
median0.0005995
Q30.1
95-th percentile0.902
Maximum0.998
Range0.998
Interquartile range (IQR)0.099995555

Descriptive statistics

Standard deviation0.30603877
Coefficient of variation (CV)1.8872343
Kurtosis1.1573595
Mean0.16216257
Median Absolute Deviation (MAD)0.0005995
Skewness1.6843391
Sum1259.3545
Variance0.093659726
MonotonicityNot monotonic
2022-11-29T17:50:54.221043image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1441
 
18.6%
0.902 14
 
0.2%
0.915 11
 
0.1%
0.873 11
 
0.1%
0.933 10
 
0.1%
0.853 10
 
0.1%
0.914 10
 
0.1%
0.886 10
 
0.1%
0.946 9
 
0.1%
0.000149 9
 
0.1%
Other values (3211) 6231
80.2%
ValueCountFrequency (%)
0 1441
18.6%
1 × 10-62
 
< 0.1%
1.01 × 10-65
 
0.1%
1.02 × 10-63
 
< 0.1%
1.03 × 10-63
 
< 0.1%
1.04 × 10-62
 
< 0.1%
1.05 × 10-62
 
< 0.1%
1.06 × 10-62
 
< 0.1%
1.07 × 10-62
 
< 0.1%
1.09 × 10-63
 
< 0.1%
ValueCountFrequency (%)
0.998 1
 
< 0.1%
0.991 1
 
< 0.1%
0.99 1
 
< 0.1%
0.985 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.979 1
 
< 0.1%
0.978 3
< 0.1%
0.976 2
< 0.1%
0.974 1
 
< 0.1%

liveness
Real number (ℝ)

Distinct1358
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.19979445
Minimum0.0146
Maximum0.999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:54.322976image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0.0146
5-th percentile0.0552
Q10.0924
median0.127
Q30.243
95-th percentile0.639
Maximum0.999
Range0.9844
Interquartile range (IQR)0.1506

Descriptive statistics

Standard deviation0.1805264
Coefficient of variation (CV)0.90356061
Kurtosis4.8015641
Mean0.19979445
Median Absolute Deviation (MAD)0.0495
Skewness2.173675
Sum1551.6037
Variance0.03258978
MonotonicityNot monotonic
2022-11-29T17:50:54.422744image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.111 85
 
1.1%
0.11 80
 
1.0%
0.105 77
 
1.0%
0.104 74
 
1.0%
0.103 66
 
0.8%
0.102 66
 
0.8%
0.112 65
 
0.8%
0.101 63
 
0.8%
0.116 63
 
0.8%
0.107 63
 
0.8%
Other values (1348) 7064
91.0%
ValueCountFrequency (%)
0.0146 1
< 0.1%
0.015 1
< 0.1%
0.0166 1
< 0.1%
0.0197 1
< 0.1%
0.0204 1
< 0.1%
0.0206 1
< 0.1%
0.0217 1
< 0.1%
0.022 1
< 0.1%
0.023 1
< 0.1%
0.0238 1
< 0.1%
ValueCountFrequency (%)
0.999 1
 
< 0.1%
0.99 1
 
< 0.1%
0.989 1
 
< 0.1%
0.988 2
< 0.1%
0.986 1
 
< 0.1%
0.985 1
 
< 0.1%
0.983 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 3
< 0.1%
0.979 3
< 0.1%

valence
Real number (ℝ)

Distinct1161
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.59599285
Minimum0
Maximum0.99
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:54.516618image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.098475
Q10.397
median0.638
Q30.824
95-th percentile0.96
Maximum0.99
Range0.99
Interquartile range (IQR)0.427

Descriptive statistics

Standard deviation0.26621086
Coefficient of variation (CV)0.44666787
Kurtosis-0.87154156
Mean0.59599285
Median Absolute Deviation (MAD)0.207
Skewness-0.43656196
Sum4628.4805
Variance0.070868221
MonotonicityNot monotonic
2022-11-29T17:50:54.618541image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.961 58
 
0.7%
0.964 46
 
0.6%
0.963 41
 
0.5%
0.96 38
 
0.5%
0.962 33
 
0.4%
0.969 28
 
0.4%
0.926 26
 
0.3%
0.966 24
 
0.3%
0.965 22
 
0.3%
0.889 21
 
0.3%
Other values (1151) 7429
95.7%
ValueCountFrequency (%)
0 2
< 0.1%
0.00336 1
< 0.1%
0.00364 1
< 0.1%
0.00457 1
< 0.1%
0.0144 1
< 0.1%
0.0149 1
< 0.1%
0.015 1
< 0.1%
0.0173 1
< 0.1%
0.0181 1
< 0.1%
0.0185 1
< 0.1%
ValueCountFrequency (%)
0.99 1
 
< 0.1%
0.988 1
 
< 0.1%
0.986 1
 
< 0.1%
0.985 1
 
< 0.1%
0.984 1
 
< 0.1%
0.982 1
 
< 0.1%
0.981 4
0.1%
0.98 4
0.1%
0.979 6
0.1%
0.978 3
< 0.1%

tempo
Real number (ℝ)

Distinct7420
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean119.00945
Minimum35.732
Maximum241.423
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:54.720513image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum35.732
5-th percentile77.421
Q198.3485
median117.3975
Q3135.085
95-th percentile173.49725
Maximum241.423
Range205.691
Interquartile range (IQR)36.7365

Descriptive statistics

Standard deviation28.381402
Coefficient of variation (CV)0.23848024
Kurtosis0.12039093
Mean119.00945
Median Absolute Deviation (MAD)18.353
Skewness0.4842546
Sum924227.37
Variance805.50396
MonotonicityNot monotonic
2022-11-29T17:50:54.812465image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
116.735 4
 
0.1%
90.584 3
 
< 0.1%
124.806 3
 
< 0.1%
103.564 3
 
< 0.1%
130.669 3
 
< 0.1%
116.112 3
 
< 0.1%
116.229 3
 
< 0.1%
129.961 3
 
< 0.1%
95.842 3
 
< 0.1%
113.229 3
 
< 0.1%
Other values (7410) 7735
99.6%
ValueCountFrequency (%)
35.732 1
< 0.1%
37.114 1
< 0.1%
46.074 1
< 0.1%
46.716 1
< 0.1%
46.744 1
< 0.1%
47.786 1
< 0.1%
47.966 1
< 0.1%
48.611 1
< 0.1%
49.194 1
< 0.1%
49.947 1
< 0.1%
ValueCountFrequency (%)
241.423 1
< 0.1%
214.848 2
< 0.1%
211.261 1
< 0.1%
209.804 1
< 0.1%
209.636 1
< 0.1%
209.611 1
< 0.1%
209.397 1
< 0.1%
208.663 1
< 0.1%
208.615 1
< 0.1%
207.639 1
< 0.1%

duration_ms
Real number (ℝ)

Distinct6402
Distinct (%)82.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239933.12
Minimum20493
Maximum3391040
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:54.904672image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum20493
5-th percentile117878.5
Q1173220
median210813.5
Q3264973.25
95-th percentile440103
Maximum3391040
Range3370547
Interquartile range (IQR)91753.25

Descriptive statistics

Standard deviation144973.63
Coefficient of variation (CV)0.60422517
Kurtosis76.114091
Mean239933.12
Median Absolute Deviation (MAD)42986.5
Skewness6.2791808
Sum1.8633206 × 109
Variance2.1017353 × 1010
MonotonicityNot monotonic
2022-11-29T17:50:55.006886image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
186600 6
 
0.1%
191067 5
 
0.1%
171000 5
 
0.1%
171960 5
 
0.1%
174667 4
 
0.1%
163693 4
 
0.1%
199027 4
 
0.1%
211667 4
 
0.1%
208467 4
 
0.1%
153973 4
 
0.1%
Other values (6392) 7721
99.4%
ValueCountFrequency (%)
20493 1
< 0.1%
25907 1
< 0.1%
27200 1
< 0.1%
27373 1
< 0.1%
28320 1
< 0.1%
30320 1
< 0.1%
31107 1
< 0.1%
31400 1
< 0.1%
32160 1
< 0.1%
33067 1
< 0.1%
ValueCountFrequency (%)
3391040 1
< 0.1%
2685093 1
< 0.1%
2623952 1
< 0.1%
2159427 1
< 0.1%
2083067 1
< 0.1%
1838933 1
< 0.1%
1788627 1
< 0.1%
1619373 1
< 0.1%
1618693 1
< 0.1%
1587800 1
< 0.1%

time_signature
Categorical

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size60.8 KiB
4
6845 
3
753 
5
 
89
1
 
79

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7766
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row3
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 6845
88.1%
3 753
 
9.7%
5 89
 
1.1%
1 79
 
1.0%

Length

2022-11-29T17:50:55.098598image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:50:55.180195image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
4 6845
88.1%
3 753
 
9.7%
5 89
 
1.1%
1 79
 
1.0%

Most occurring characters

ValueCountFrequency (%)
4 6845
88.1%
3 753
 
9.7%
5 89
 
1.1%
1 79
 
1.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 6845
88.1%
3 753
 
9.7%
5 89
 
1.1%
1 79
 
1.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 6845
88.1%
3 753
 
9.7%
5 89
 
1.1%
1 79
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 6845
88.1%
3 753
 
9.7%
5 89
 
1.1%
1 79
 
1.0%

chorus_hit
Real number (ℝ)

Distinct7655
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.847828
Minimum0
Maximum220.0245
Zeros39
Zeros (%)0.5%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:55.262418image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile19.70265
Q127.74209
median35.8791
Q347.213043
95-th percentile74.390705
Maximum220.0245
Range220.0245
Interquartile range (IQR)19.470953

Descriptive statistics

Standard deviation18.287632
Coefficient of variation (CV)0.45893675
Kurtosis6.760641
Mean39.847828
Median Absolute Deviation (MAD)9.34089
Skewness1.8581501
Sum309458.23
Variance334.4375
MonotonicityNot monotonic
2022-11-29T17:50:55.354306image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39
 
0.5%
24.04446 3
 
< 0.1%
39.55256 2
 
< 0.1%
42.79951 2
 
< 0.1%
22.58 2
 
< 0.1%
34.06918 2
 
< 0.1%
44.38604 2
 
< 0.1%
33.37936 2
 
< 0.1%
56.91646 2
 
< 0.1%
37.02618 2
 
< 0.1%
Other values (7645) 7708
99.3%
ValueCountFrequency (%)
0 39
0.5%
4.51267 1
 
< 0.1%
5.38553 1
 
< 0.1%
6.1204 1
 
< 0.1%
7.69751 1
 
< 0.1%
7.85089 1
 
< 0.1%
7.87196 1
 
< 0.1%
8.02596 1
 
< 0.1%
8.05336 1
 
< 0.1%
8.2182 1
 
< 0.1%
ValueCountFrequency (%)
220.0245 1
< 0.1%
188.30605 1
< 0.1%
176.53045 1
< 0.1%
175.76944 1
< 0.1%
174.60947 1
< 0.1%
172.46549 1
< 0.1%
157.28203 1
< 0.1%
150.20672 1
< 0.1%
149.30319 1
< 0.1%
148.8634 1
< 0.1%

sections
Real number (ℝ)

Distinct66
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.76101
Minimum0
Maximum130
Zeros5
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size60.8 KiB
2022-11-29T17:50:55.446266image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5
Q18
median10
Q312
95-th percentile19
Maximum130
Range130
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.6421333
Coefficient of variation (CV)0.52431264
Kurtosis62.130428
Mean10.76101
Median Absolute Deviation (MAD)2
Skewness5.4068049
Sum83570
Variance31.833668
MonotonicityNot monotonic
2022-11-29T17:50:55.548965image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9 1117
14.4%
8 994
12.8%
10 965
12.4%
11 795
10.2%
7 653
8.4%
12 603
7.8%
13 454
 
5.8%
6 405
 
5.2%
14 309
 
4.0%
5 225
 
2.9%
Other values (56) 1246
16.0%
ValueCountFrequency (%)
0 5
 
0.1%
1 3
 
< 0.1%
2 31
 
0.4%
3 63
 
0.8%
4 128
 
1.6%
5 225
 
2.9%
6 405
 
5.2%
7 653
8.4%
8 994
12.8%
9 1117
14.4%
ValueCountFrequency (%)
130 1
< 0.1%
101 1
< 0.1%
89 1
< 0.1%
82 1
< 0.1%
74 1
< 0.1%
70 2
< 0.1%
69 1
< 0.1%
64 1
< 0.1%
63 2
< 0.1%
62 2
< 0.1%

target
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size60.8 KiB
0
3883 
1
3883 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters7766
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 3883
50.0%
1 3883
50.0%

Length

2022-11-29T17:50:55.640912image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-11-29T17:50:55.711754image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
ValueCountFrequency (%)
0 3883
50.0%
1 3883
50.0%

Most occurring characters

ValueCountFrequency (%)
0 3883
50.0%
1 3883
50.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7766
100.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3883
50.0%
1 3883
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7766
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3883
50.0%
1 3883
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7766
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3883
50.0%
1 3883
50.0%

Interactions

2022-11-29T17:50:50.804367image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:36.598403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:38.238847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.357457image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:40.544963image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.641316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.728118image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:43.984954image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.087785image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.191479image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:47.310339image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.560536image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:49.716496image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:50.889047image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:36.683086image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:38.316983image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.435755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:40.638725image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.710357image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.812764image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:44.069627image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.172433image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.276140image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:47.379409image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.645183image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:49.801311image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:50.973698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:36.767728image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:38.401661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.519934image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:40.723365image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.795007image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.897443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:44.154261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.257105image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.360435image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:47.464048image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.745450image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:49.886054image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:51.051847image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:36.852369image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:38.486336image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.604599image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:40.792387image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.879688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.982084image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:44.232417image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.350868image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.454212image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:47.542203image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.830095image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:49.964124image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:51.137000image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:36.930512image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:38.571021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.673723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:40.876755image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.964372image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:43.066780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:44.317079image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.435516image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.538878image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:47.811730image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.914794image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:50.048786image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:51.221578image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:37.015403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:38.655688image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.758348image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:40.961645image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.049021image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:43.151587image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:44.401580image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.504550image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.623554image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:47.880769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.999475image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:50.133295image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:51.306261image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:37.646091image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:38.740358image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.842994image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.046082image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.127174image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:43.229741image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:44.486144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.589243image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.708217image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:47.965443image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:49.084316image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:50.217809image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:51.390969image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:37.727193image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:38.840919image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.921146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.124231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.211477image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:43.314622image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:44.570821image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.689698image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.792895image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.059570image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:49.184107image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:50.302490image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:51.490887image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:37.815535image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:38.919073image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:40.005767image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.208902image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.296051image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:43.415135image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:44.670765image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.774341image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.877559image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.143710image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:49.268436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:50.387136image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:51.575522image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:37.900189image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.003460image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:40.090197image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.293571image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.380401image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:43.484080image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:44.755421image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.858980image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.962432image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.228374image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:49.362198image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:50.465436image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:51.653657image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:37.969199image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.088002image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:40.174858image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.378231image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.465081image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:43.715609image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:44.833577image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.937132image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:47.040586image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.297468image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:49.447237image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:50.550403image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:51.753925image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:38.069504image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.188309image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:40.375447image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.462913image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.549711image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:43.815593image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:44.918301image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.037723image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:47.125248image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.397769image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:49.547146image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:50.634962image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:51.838603image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:38.154181image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:39.257680image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:40.460296image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:41.547548image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:42.643474image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:43.900272image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:45.003144image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:46.106780image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:47.225661image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:48.466772image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:49.631814image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
2022-11-29T17:50:50.719702image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Correlations

2022-11-29T17:50:55.782430image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-11-29T17:50:55.934208image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-11-29T17:50:56.087292image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-11-29T17:50:56.238340image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-11-29T17:50:56.372808image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-11-29T17:50:56.454481image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-11-29T17:50:51.976350image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
A simple visualization of nullity by column.
2022-11-29T17:50:52.170605image/svg+xmlMatplotlib v3.5.1, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
0Porque?Reginaldo Rossispotify:track:28KC2wl7jDv6Ms1Uc0OMpm0.6690.5471-9.87300.05760.35300.0000000.05150.730131.073141067428.7976360
1Free JazzMaria Teresa Lucianispotify:track:0zagn8PNnM7pM8pz1gXOAD0.2910.3001-14.05400.03260.90500.4920000.11800.03975.619228224444.1030390
2Reject of SocietyCrassspotify:track:18eNASEiuAhO2ML5LBIoG40.3550.9687-4.68710.18900.05040.0000000.13000.768181.29166800325.2300460
3Yeah !The Jetsspotify:track:1UlJzHc8E5EprQFJxjSyPL0.5980.8916-11.90410.05520.01830.8360000.57900.826144.383155395455.6206670
4Gotta Find A WayThe Momentsspotify:track:1Jtlirju8tUXTtvyZJAN5v0.4400.3765-10.93910.04820.34800.0000000.08150.358130.438218333434.90979121
5What Shall We Do When We All Go Out?Mike Seegerspotify:track:3mjhB6xOe5d8HfWbUFApHR0.6180.1677-16.75510.04510.82800.0000030.19700.651175.951132667458.2793570
6Beautiful SundayDaniel Boonespotify:track:6DqNZJ4hhBYd8qj9WbpSTJ0.6760.4639-13.15510.03060.30900.0000000.28800.910121.923182133436.2869771
7The Man With The Child In His EyesKate Bushspotify:track:4xjVfArXNQRxAvsUpjmfMt0.2490.2610-10.59210.03340.96200.0015400.13600.19590.007158693433.6234181
8Psychedelic ShackThe Temptationsspotify:track:5kx4xMErn45NZvEChKiqdo0.6630.8225-13.11900.05900.19900.0000090.06260.818108.940230293460.0556881
9Tell It Like It IsAndy Williamsspotify:track:7MYHtpI1ebzt1lkAxgNDEZ0.5220.4406-13.25800.03500.43400.0000000.21800.29597.475159760338.6946771
trackartisturidanceabilityenergykeyloudnessmodespeechinessacousticnessinstrumentalnesslivenessvalencetempoduration_mstime_signaturechorus_hitsectionstarget
7756Insight - 2007 RemasterJoy Divisionspotify:track:7oNu4gShXbbM1GBJtfJucq0.6360.4762-12.58210.03480.021100.8050000.52200.164131.220266280439.8114080
7757Des Éclats de LuneLaurence Vanayspotify:track:3VFbMzVJxqhBRYMdQaHJJ80.6920.2617-20.50200.03220.136000.6920000.09550.240117.364229560433.65055110
7758My Eyes Adored YouFrankie Vallispotify:track:2yDQVcj26tpi9IUJhw9xDs0.4610.49911-10.44300.03160.238000.0000000.13500.387137.687212013437.30521101
7759SaraFleetwood Macspotify:track:0YuCATlVwxG5Mq8lN2Omro0.7350.53410-12.15410.02950.423000.0026700.20400.551127.010275107456.88777101
7760Call Me (Come Back Home)Al Greenspotify:track:2THyR0on3DrsfXBtRrLZgK0.6830.39210-13.21310.02930.596000.0000030.22700.67999.790184907429.70104111
7761Born with the BluesBuster Bentonspotify:track:2ZARTKpOvYYuR2DxcTjxQo0.3730.4179-12.27300.09340.858000.4620000.04250.588174.359289653336.12884140
7762Farther On Down The RoadJoe Simonspotify:track:1Fj16GGVNFdPut4SFG2vvp0.4700.4902-11.63010.09150.064900.0000010.05740.644176.523187067433.4138091
7763SweetheartEngelbert Humperdinckspotify:track:6zm0XoxnzzdFeXeYGE8gFh0.2530.5502-7.74310.03050.155000.0000210.07860.643100.011183613422.28247121
7764Until It's Time For You To GoNeil Diamondspotify:track:6fsAxEHvbVPU8pVPnBXaX20.4190.1240-21.66610.03400.800000.0001000.13100.17891.439211440319.82409101
7765Try Me, I Know We Can Make ItDonna Summerspotify:track:75y3G2V8wSpm89einsZ4d50.8100.6020-15.67400.03630.004260.3270000.09200.964122.952198093441.00309101